OpenRaiser/Intern-Atlas huggingface dataset:
Capacity of Burst Noise-Erasure Channels With and Without Feedback and Input Cost
Venue: arXiv cs.IT 2017
Year: 2017

A class of burst noise-erasure channels which incorporate both errors and erasures during transmission is studied. The channel, whose output is explicitly expressed in terms of its input and a stationary ergodic noise-erasure process, is shown to have a so-called "quasi-symmetry" property under certain invertibility conditions. As a result, it is proved that a uniformly distributed input process maximizes the channel's block mutual information, resulting in a closed-form formula for its non-feedback capacity in terms of the noise-erasure entropy rate and the entropy rate of an auxiliary erasure process. The feedback channel capacity is also characterized, showing that feedback does not increase capacity and generalizing prior related results. The capacity-cost function of the channel with and without feedback is also investigated. A sequence of finite-letter upper bounds for the capacity-cost function without feedback is derived. Finite-letter lower bonds for the capacity-cost function with feedback are obtained using a specific encoding rule. Based on these bounds, it is demonstrated both numerically and analytically that feedback can increase the capacity-cost function for a class of channels with Markov noise-erasure processes.
Execution Templates: Caching Control Plane Decisions for Strong Scaling of Data Analytics
Venue: arXiv cs.DC 2017
Year: 2017

Control planes of cloud frameworks trade off between scheduling granularity and performance. Centralized systems schedule at task granularity, but only schedule a few thousand tasks per second. Distributed systems schedule hundreds of thousands of tasks per second but changing the schedule is costly. We present execution templates, a control plane abstraction that can schedule hundreds of thousands of tasks per second while supporting fine-grained, per-task scheduling decisions. Execution templates leverage a program's repetitive control flow to cache blocks of frequently-executed tasks. Executing a task in a template requires sending a single message. Large-scale scheduling changes install new templates, while small changes apply edits to existing templates. Evaluations of execution templates in Nimbus, a data analytics framework, find that they provide the fine-grained scheduling flexibility of centralized control planes while matching the strong scaling of distributed ones. Execution templates support complex, real-world applications, such as a fluid simulation with a triply nested loop and data dependent branches.
Towards Simulation and Risk Assessment of Weather-Related Cascading Outages
Venue: arXiv cs.CE 2017
Year: 2017

Weather and environmental factors are verified to have played significant roles in historical major cascading outages and blackouts. Therefore, in the simulation and risk assessment of cascading outages in power systems, it is necessary to consider the weather and environmental effects. This paper proposes a method for the risk assessment of weather-related cascading outages. Based on the analysis of historical outage records and temperature-dependent physical outage mechanisms of transmission lines, an outage rate model considering weather condition and conductor temperature is proposed, and the analytical form of outage probability of lines are derived. With the weather-dependent outage model, a two-stage risk assessment method based on Markovian tree (MT) search is proposed, which consists of offline full assessment, and online efficient update of risk assessment results and continued MT search using updated NWP data. The test cases on NPCC 140-bus test system model in winter and summer scenarios verify the advantages of the proposed risk assessment method in both accuracy and efficiency.
3GPP-inspired HetNet Model using Poisson Cluster Process: Sum-product Functionals and Downlink Coverage
Venue: arXiv cs.IT 2017
Year: 2017

The growing complexity of heterogeneous cellular networks (HetNets) has necessitated a variety of user and base station (BS) configurations to be considered for realistic performance evaluation and system design. This is directly reflected in the HetNet simulation models proposed by standardization bodies, such as the third generation partnership project (3GPP). Complementary to these simulation models, stochastic geometry-based approach, modeling the locations of the users and the K tiers of BSs as independent and homogeneous Poisson point processes (PPPs), has gained prominence in the past few years. Despite its success in revealing useful insights, this PPP-based K-tier HetNet model is not rich enough to capture spatial coupling between user and BS locations that exists in real-world HetNet deployments and is included in 3GPP simulation models. In this paper, we demonstrate that modeling a fraction of users and arbitrary number of BS tiers alternatively with a Poisson cluster process (PCP) captures the aforementioned coupling, thus bridging the gap between the 3GPP simulation models and the PPP-based analytic model for HetNets. We further show that the downlink coverage probability of a typical user under maximum signal-to-interference-ratio association can be expressed in terms of the sum-product functionals over PPP, PCP, and its associated offspring point process, which are all characterized as a part of our analysis. We also show that the proposed model converges to the PPP-based HetNet model as the cluster size of the PCPs tends to infinity. Finally, we specialize our analysis based on general PCPs for Thomas and Matern cluster processes. Special instances of the proposed model closely resemble the different configurations for BS and user locations considered in 3GPP simulations.
Pixie: A heterogeneous Virtual Coarse-Grained Reconfigurable Array for high performance image processing applications
Venue: arXiv cs.AR 2017
Year: 2017

Coarse-Grained Reconfigurable Arrays (CGRAs) enable ease of programmability and result in low development costs. They enable the ease of use specifically in reconfigurable computing applications. The smaller cost of compilation and reduced reconfiguration overhead enables them to become attractive platforms for accelerating high-performance computing applications such as image processing. The CGRAs are ASICs and therefore, expensive to produce. However, Field Programmable Gate Arrays (FPGAs) are relatively cheaper for low volume products but they are not so easily programmable. We combine best of both worlds by implementing a Virtual Coarse-Grained Reconfigurable Array (VCGRA) on FPGA. VCGRAs are a trade off between FPGA with large routing overheads and ASICs. In this perspective we present a novel heterogeneous Virtual Coarse-Grained Reconfigurable Array (VCGRA) called "Pixie" which is suitable for implementing high performance image processing applications. The proposed VCGRA contains generic processing elements and virtual channels that are described using the Hardware Description Language VHDL. Both elements have been optimized by using the parameterized configuration tool flow and result in a resource reduction of 24% for each processing elements and 82% for each virtual channels respectively.
Deep 360 Pilot: Learning a Deep Agent for Piloting through 360° Sports Video
Venue: arXiv cs.CV 2017
Year: 2017

Watching a 360° sports video requires a viewer to continuously select a viewing angle, either through a sequence of mouse clicks or head movements. To relieve the viewer from this "360 piloting" task, we propose "deep 360 pilot" -- a deep learning-based agent for piloting through 360° sports videos automatically. At each frame, the agent observes a panoramic image and has the knowledge of previously selected viewing angles. The task of the agent is to shift the current viewing angle (i.e. action) to the next preferred one (i.e., goal). We propose to directly learn an online policy of the agent from data. We use the policy gradient technique to jointly train our pipeline: by minimizing (1) a regression loss measuring the distance between the selected and ground truth viewing angles, (2) a smoothness loss encouraging smooth transition in viewing angle, and (3) maximizing an expected reward of focusing on a foreground object. To evaluate our method, we build a new 360-Sports video dataset consisting of five sports domains. We train domain-specific agents and achieve the best performance on viewing angle selection accuracy and transition smoothness compared to [51] and other baselines.
Pixel Normalization from Numeric Data as Input to Neural Networks
Venue: arXiv cs.CV 2017
Year: 2017

Text to image transformation for input to neural networks requires intermediate steps. This paper attempts to present a new approach to pixel normalization so as to convert textual data into image, suitable as input for neural networks. This method can be further improved by its Graphics Processing Unit (GPU) implementation to provide significant speedup in computational time.
Towards a Physical Oracle for the Partition Problem using Analogue Computing
Venue: arXiv cs.CC 2017
Year: 2017

Despite remarkable achievements in its practical tractability, the notorious class of NP-complete problems has been escaping all attempts to find a worst-case polynomial time-bound solution algorithms for any of them. The vast majority of work relies on Turing machines or equivalent models, all of which relate to digital computing. This raises the question of whether a computer that is (partly) non-digital could offer a new door towards an efficient solution. And indeed, the partition problem, which is another NP-complete sibling of the famous Boolean satisfiability problem SAT, might be open to efficient solutions using analogue computing. We investigate this hypothesis here, providing experimental evidence that Partition, and in turn also SAT, may become tractable on a combined digital and analogue computing machine. This work provides mostly theoretical and based on simulations, and as such does not exhibit a polynomial time algorithm to solve NP-complete problems. Instead, it is intended as a pointer to new directions of research on special-purpose computing architectures that may help handling the class NP efficiently.
A Hybrid Approach to Video Source Identification
Venue: arXiv cs.MM 2017
Year: 2017

Multimedia Forensics allows to determine whether videos or images have been captured with the same device, and thus, eventually, by the same person. Currently, the most promising technology to achieve this task, exploits the unique traces left by the camera sensor into the visual content. Anyway, image and video source identification are still treated separately from one another. This approach is limited and anachronistic if we consider that most of the visual media are today acquired using smartphones, that capture both images and videos. In this paper we overcome this limitation by exploring a new approach that allows to synergistically exploit images and videos to study the device from which they both come. Indeed, we prove it is possible to identify the source of a digital video by exploiting a reference sensor pattern noise generated from still images taken by the same device of the query video. The proposed method provides comparable or even better performance, when compared to the current video identification strategies, where a reference pattern is estimated from video frames. We also show how this strategy can be effective even in case of in-camera digitally stabilized videos, where a non-stabilized reference is not available, by solving some state-of-the-art limitations. We explore a possible direct application of this result, that is social media profile linking, i.e. discovering relationships between two or more social media profiles by comparing the visual contents - images or videos - shared therein.
Semi-supervised model-based clustering with controlled clusters leakage
Venue: arXiv cs.LG 2017
Year: 2017

In this paper, we focus on finding clusters in partially categorized data sets. We propose a semi-supervised version of Gaussian mixture model, called C3L, which retrieves natural subgroups of given categories. In contrast to other semi-supervised models, C3L is parametrized by user-defined leakage level, which controls maximal inconsistency between initial categorization and resulting clustering. Our method can be implemented as a module in practical expert systems to detect clusters, which combine expert knowledge with true distribution of data. Moreover, it can be used for improving the results of less flexible clustering techniques, such as projection pursuit clustering. The paper presents extensive theoretical analysis of the model and fast algorithm for its efficient optimization. Experimental results show that C3L finds high quality clustering model, which can be applied in discovering meaningful groups in partially classified data.
Hardware emulation of stochastic p-bits for invertible logic
Venue: arXiv cs.ET 2017
Year: 2017

The common feature of nearly all logic and memory devices is that they make use of stable units to represent 0's and 1's. A completely different paradigm is based on three-terminal stochastic units which could be called "p-bits", where the output is a random telegraphic signal continuously fluctuating between 0 and 1 with a tunable mean. p-bits can be interconnected to receive weighted contributions from others in a network, and these weighted contributions can be chosen to not only solve problems of optimization and inference but also to implement precise Boolean functions in an inverted mode. This inverted operation of Boolean gates is particularly striking: They provide inputs consistent to a given output along with unique outputs to a given set of inputs. The existing demonstrations of accurate invertible logic are intriguing, but will these striking properties observed in computer simulations carry over to hardware implementations? This paper uses individual micro controllers to emulate p-bits, and we present results for a 4-bit ripple carry adder with 48 p-bits and a 4-bit multiplier with 46 p-bits working in inverted mode as a factorizer. Our results constitute a first step towards implementing p-bits with nano devices, like stochastic Magnetic Tunnel Junctions.
Multi-Stage Holomorphic Embedding Method for Calculating the Power-Voltage Curve
Venue: arXiv cs.SY 2017
Year: 2017

The recently proposed non-iterative load flow method, called the holomorphic embedding method, may encounter the precision issue, i.e. nontrivial round-off errors caused by the limit of digits used in computation when calculating the power-voltage (P-V) curve for a heavily loaded power system. This letter proposes a multi-stage scheme to solve such a precision issue and calculate an accurate P-V curve. The scheme is verified on the New Eng-land 39-bus power system and benchmarked with the result from the traditional continuation power flow method.
Safety and Security for Intelligent Infrastructure
Venue: arXiv cs.CY 2017
Year: 2017

Increasingly, smart computing devices, with powerful sensors and internet connectivity, are being embedded into all new forms of infrastructure, from hospitals to roads to factories. These devices are part of the Internet of Things (IoT) and the economic value of their widespread deployment is estimated to be trillions of dollars, with billions of devices deployed. Consider the example of "smart meters" for electricity utilities. Because of clear economic benefits, including a reduction in the cost of reading meters, more precise information about outages and diagnostics, and increased benefits from predicting and balancing electric loads, such meters are already being rolled out across North America. With residential solar collection, smart meters allow individuals to sell power back to the grid providing economic incentives for conservation. Similarly, smart water meters allow water conservation in a drought. Such infrastructure upgrades are infrequent (with smart meters expected to be in service for 20-30 years) but the benefits from the upgrade justify the significant cost. A long-term benefit of such upgrades is that unforeseen savings might be realized in the future when new analytic techniques are applied to the data that is collected. The same benefits accrue to any infrastructure that embeds increased sensing and actuation capabilities via IoT devices, including roads and traffic control, energy and water management in buildings, and public health monitoring.
Crowdsourcing Argumentation Structures in Chinese Hotel Reviews
Venue: arXiv cs.CL 2017
Year: 2017

Argumentation mining aims at automatically extracting the premises-claim discourse structures in natural language texts. There is a great demand for argumentation corpora for customer reviews. However, due to the controversial nature of the argumentation annotation task, there exist very few large-scale argumentation corpora for customer reviews. In this work, we novelly use the crowdsourcing technique to collect argumentation annotations in Chinese hotel reviews. As the first Chinese argumentation dataset, our corpus includes 4814 argument component annotations and 411 argument relation annotations, and its annotations qualities are comparable to some widely used argumentation corpora in other languages.
Optimizing noise level for perturbing geo-location data
Venue: arXiv cs.CR 2017
Year: 2017

With the tremendous increase in the number of smart phones, app stores have been overwhelmed with applications requiring geo-location access in order to provide their users better services through personalization. Revealing a user's location to these third party apps, no matter at what frequency, is a severe privacy breach which can have unpleasant social consequences. In order to prevent inference attacks derived from geo-location data, a number of location obfuscation techniques have been proposed in the literature. However, none of them provides any objective measure of privacy guarantee. Some work has been done to define differential privacy for geo-location data in the form of geo-indistinguishability with l privacy guarantee. These techniques do not utilize any prior background information about the Points of Interest (PoIs) of a user and apply Laplacian noise to perturb all the location coordinates. Intuitively, the utility of such a mechanism can be improved if the noise distribution is derived after considering some prior information about PoIs. In this paper, we apply the standard definition of differential privacy on geo-location data. We use first principles to model various privacy and utility constraints, prior background information available about the PoIs (distribution of PoI locations in a 1D plane) and the granularity of the input required by different types of apps, in order to produce a more accurate and a utility maximizing differentially private algorithm for geo-location data at the OS level. We investigate this for a particular category of apps and for some specific scenarios. This will also help us to verify that whether Laplacian noise is still the optimal perturbation when we have such prior information.
Discrete Modeling of Multi-Transmitter Neural Networks with Neuron Competition
Venue: arXiv cs.NE 2017
Year: 2017

We propose a novel discrete model of central pattern generators (CPG), neuronal ensembles generating rhythmic activity. The model emphasizes the role of nonsynaptic interactions and the diversity of electrical properties in nervous systems. Neurons in the model release different neurotransmitters into the shared extracellular space (ECS) so each neuron with the appropriate set of receptors can receive signals from other neurons. We consider neurons, differing in their electrical activity, represented as finite-state machines functioning in discrete time steps. Discrete modeling is aimed to provide a computationally tractable and compact explanation of rhythmic pattern generation in nervous systems. The important feature of the model is the introduced mechanism of neuronal competition which is shown to be responsible for the generation of proper rhythms. The model is illustrated with two examples: a half-center oscillator considered to be a basic mechanism of emerging rhythmic activity and the well-studied feeding network of a pond snail. Future research will focus on the neuromodulatory effects ubiquitous in CPG networks and the whole nervous systems.
Hidden space reconstruction inspires link prediction in complex networks
Venue: arXiv cs.SI 2017
Year: 2017

As a fundamental challenge in vast disciplines, link prediction aims to identify potential links in a network based on the incomplete observed information, which has broad applications ranging from uncovering missing protein-protein interaction to predicting the evolution of networks. One of the most influential methods rely on similarity indices characterized by the common neighbors or its variations. We construct a hidden space mapping a network into Euclidean space based solely on the connection structures of a network. Compared with real geographical locations of nodes, our reconstructed locations are in conformity with those real ones. The distances between nodes in our hidden space could serve as a novel similarity metric in link prediction. In addition, we hybrid our hidden space method with other state-of-the-art similarity methods which substantially outperforms the existing methods on the prediction accuracy. Hence, our hidden space reconstruction model provides a fresh perspective to understand the network structure, which in particular casts a new light on link prediction.
Data Readiness Levels
Venue: arXiv cs.DB 2017
Year: 2017

Application of models to data is fraught. Data-generating collaborators often only have a very basic understanding of the complications of collating, processing and curating data. Challenges include: poor data collection practices, missing values, inconvenient storage mechanisms, intellectual property, security and privacy. All these aspects obstruct the sharing and interconnection of data, and the eventual interpretation of data through machine learning or other approaches. In project reporting, a major challenge is in encapsulating these problems and enabling goals to be built around the processing of data. Project overruns can occur due to failure to account for the amount of time required to curate and collate. But to understand these failures we need to have a common language for assessing the readiness of a particular data set. This position paper proposes the use of data readiness levels: it gives a rough outline of three stages of data preparedness and speculates on how formalisation of these levels into a common language for data readiness could facilitate project management.